R Markdown

Welcome to the project of “PROGRAMMING - MBD-EN-BL2021J-1_32R377_406729” course. In this project I will be working with a real-world dataset from the Kaggle platform corresponding to the AMS 2013-2014 Solar Energy Prediction Contest.

The goal in this project will be:

  1. To perform an exploratory data analysis, EDA.

  2. Create an R Markdown report with the EDA findings.

Each section will be split in every indivdual dataset that was looked at. I will particularily focus on the main dataset: solar_dataset.

About this dataset

This data is about the solar energy production per stations in Oklahoma Mesonet sites.

This task includes 3 datasets.

Start of EDA

First look at the data

First I will take an initial look at the data and the dimensions. In the following order.

  • solar_dataset

  • additional_variables

  • station_info

Show dimensions of the dataset:

Rows: 6909 Columns: 456

Rows: 6909 Columns: 101

Rows: 98 Columns: 4

Clearly, the dataset is huge and probably, based on the explanation found in Kaggle, the data will be very skewed. Which indicates that identifying outliers based on the data will be hard.

solar_dataset

First look at the dataset.

Clearly, the data is not completely clean here. I can see many null values and also PCA features

  • The date data seems to be oddly formatted. Which needs to be formatted properly.
  • The data itself seems to be OK.
This is the head
Date ACME ADAX ALTU APAC ARNE BEAV BESS BIXB BLAC BOIS BOWL BREC BRIS BUFF BURB BURN BUTL BYAR CAMA CENT CHAN CHER CHEY CHIC CLAY CLOU COOK COPA DURA ELRE ERIC EUFA FAIR FORA FREE FTCB GOOD GUTH HASK HINT HOBA HOLL HOOK HUGO IDAB JAYX KENT KETC LAHO LANE MADI MANG MARE MAYR MCAL MEDF MEDI MIAM MINC MTHE NEWK NINN NOWA OILT OKEM OKMU PAUL PAWN PERK PRYO PUTN REDR RETR RING SALL SEIL SHAW SKIA SLAP SPEN STIG STIL STUA SULP TAHL TALI TIPT TISH VINI WASH WATO WAUR WEAT WEST WILB WIST WOOD WYNO PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15 PC16 PC17 PC18 PC19 PC20 PC21 PC22 PC23 PC24 PC25 PC26 PC27 PC28 PC29 PC30 PC31 PC32 PC33 PC34 PC35 PC36 PC37 PC38 PC39 PC40 PC41 PC42 PC43 PC44 PC45 PC46 PC47 PC48 PC49 PC50 PC51 PC52 PC53 PC54 PC55 PC56 PC57 PC58 PC59 PC60 PC61 PC62 PC63 PC64 PC65 PC66 PC67 PC68 PC69 PC70 PC71 PC72 PC73 PC74 PC75 PC76 PC77 PC78 PC79 PC80 PC81 PC82 PC83 PC84 PC85 PC86 PC87 PC88 PC89 PC90 PC91 PC92 PC93 PC94 PC95 PC96 PC97 PC98 PC99 PC100 PC101 PC102 PC103 PC104 PC105 PC106 PC107 PC108 PC109 PC110 PC111 PC112 PC113 PC114 PC115 PC116 PC117 PC118 PC119 PC120 PC121 PC122 PC123 PC124 PC125 PC126 PC127 PC128 PC129 PC130 PC131 PC132 PC133 PC134 PC135 PC136 PC137 PC138 PC139 PC140 PC141 PC142 PC143 PC144 PC145 PC146 PC147 PC148 PC149 PC150 PC151 PC152 PC153 PC154 PC155 PC156 PC157 PC158 PC159 PC160 PC161 PC162 PC163 PC164 PC165 PC166 PC167 PC168 PC169 PC170 PC171 PC172 PC173 PC174 PC175 PC176 PC177 PC178 PC179 PC180 PC181 PC182 PC183 PC184 PC185 PC186 PC187 PC188 PC189 PC190 PC191 PC192 PC193 PC194 PC195 PC196 PC197 PC198 PC199 PC200 PC201 PC202 PC203 PC204 PC205 PC206 PC207 PC208 PC209 PC210 PC211 PC212 PC213 PC214 PC215 PC216 PC217 PC218 PC219 PC220 PC221 PC222 PC223 PC224 PC225 PC226 PC227 PC228 PC229 PC230 PC231 PC232 PC233 PC234 PC235 PC236 PC237 PC238 PC239 PC240 PC241 PC242 PC243 PC244 PC245 PC246 PC247 PC248 PC249 PC250 PC251 PC252 PC253 PC254 PC255 PC256 PC257 PC258 PC259 PC260 PC261 PC262 PC263 PC264 PC265 PC266 PC267 PC268 PC269 PC270 PC271 PC272 PC273 PC274 PC275 PC276 PC277 PC278 PC279 PC280 PC281 PC282 PC283 PC284 PC285 PC286 PC287 PC288 PC289 PC290 PC291 PC292 PC293 PC294 PC295 PC296 PC297 PC298 PC299 PC300 PC301 PC302 PC303 PC304 PC305 PC306 PC307 PC308 PC309 PC310 PC311 PC312 PC313 PC314 PC315 PC316 PC317 PC318 PC319 PC320 PC321 PC322 PC323 PC324 PC325 PC326 PC327 PC328 PC329 PC330 PC331 PC332 PC333 PC334 PC335 PC336 PC337 PC338 PC339 PC340 PC341 PC342 PC343 PC344 PC345 PC346 PC347 PC348 PC349 PC350 PC351 PC352 PC353 PC354 PC355 PC356 PC357
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This is the tail
Date ACME ADAX ALTU APAC ARNE BEAV BESS BIXB BLAC BOIS BOWL BREC BRIS BUFF BURB BURN BUTL BYAR CAMA CENT CHAN CHER CHEY CHIC CLAY CLOU COOK COPA DURA ELRE ERIC EUFA FAIR FORA FREE FTCB GOOD GUTH HASK HINT HOBA HOLL HOOK HUGO IDAB JAYX KENT KETC LAHO LANE MADI MANG MARE MAYR MCAL MEDF MEDI MIAM MINC MTHE NEWK NINN NOWA OILT OKEM OKMU PAUL PAWN PERK PRYO PUTN REDR RETR RING SALL SEIL SHAW SKIA SLAP SPEN STIG STIL STUA SULP TAHL TALI TIPT TISH VINI WASH WATO WAUR WEAT WEST WILB WIST WOOD WYNO PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15 PC16 PC17 PC18 PC19 PC20 PC21 PC22 PC23 PC24 PC25 PC26 PC27 PC28 PC29 PC30 PC31 PC32 PC33 PC34 PC35 PC36 PC37 PC38 PC39 PC40 PC41 PC42 PC43 PC44 PC45 PC46 PC47 PC48 PC49 PC50 PC51 PC52 PC53 PC54 PC55 PC56 PC57 PC58 PC59 PC60 PC61 PC62 PC63 PC64 PC65 PC66 PC67 PC68 PC69 PC70 PC71 PC72 PC73 PC74 PC75 PC76 PC77 PC78 PC79 PC80 PC81 PC82 PC83 PC84 PC85 PC86 PC87 PC88 PC89 PC90 PC91 PC92 PC93 PC94 PC95 PC96 PC97 PC98 PC99 PC100 PC101 PC102 PC103 PC104 PC105 PC106 PC107 PC108 PC109 PC110 PC111 PC112 PC113 PC114 PC115 PC116 PC117 PC118 PC119 PC120 PC121 PC122 PC123 PC124 PC125 PC126 PC127 PC128 PC129 PC130 PC131 PC132 PC133 PC134 PC135 PC136 PC137 PC138 PC139 PC140 PC141 PC142 PC143 PC144 PC145 PC146 PC147 PC148 PC149 PC150 PC151 PC152 PC153 PC154 PC155 PC156 PC157 PC158 PC159 PC160 PC161 PC162 PC163 PC164 PC165 PC166 PC167 PC168 PC169 PC170 PC171 PC172 PC173 PC174 PC175 PC176 PC177 PC178 PC179 PC180 PC181 PC182 PC183 PC184 PC185 PC186 PC187 PC188 PC189 PC190 PC191 PC192 PC193 PC194 PC195 PC196 PC197 PC198 PC199 PC200 PC201 PC202 PC203 PC204 PC205 PC206 PC207 PC208 PC209 PC210 PC211 PC212 PC213 PC214 PC215 PC216 PC217 PC218 PC219 PC220 PC221 PC222 PC223 PC224 PC225 PC226 PC227 PC228 PC229 PC230 PC231 PC232 PC233 PC234 PC235 PC236 PC237 PC238 PC239 PC240 PC241 PC242 PC243 PC244 PC245 PC246 PC247 PC248 PC249 PC250 PC251 PC252 PC253 PC254 PC255 PC256 PC257 PC258 PC259 PC260 PC261 PC262 PC263 PC264 PC265 PC266 PC267 PC268 PC269 PC270 PC271 PC272 PC273 PC274 PC275 PC276 PC277 PC278 PC279 PC280 PC281 PC282 PC283 PC284 PC285 PC286 PC287 PC288 PC289 PC290 PC291 PC292 PC293 PC294 PC295 PC296 PC297 PC298 PC299 PC300 PC301 PC302 PC303 PC304 PC305 PC306 PC307 PC308 PC309 PC310 PC311 PC312 PC313 PC314 PC315 PC316 PC317 PC318 PC319 PC320 PC321 PC322 PC323 PC324 PC325 PC326 PC327 PC328 PC329 PC330 PC331 PC332 PC333 PC334 PC335 PC336 PC337 PC338 PC339 PC340 PC341 PC342 PC343 PC344 PC345 PC346 PC347 PC348 PC349 PC350 PC351 PC352 PC353 PC354 PC355 PC356 PC357
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6909 20121130 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA -143.9837 8.912251 -8.723154 -20.92404 -78.32949 10.927778 -47.237408 10.7305262 27.793989 -17.319413 2.7050112 -28.63918 -6.598601 10.241989 6.500161 -6.364617 -4.303563 3.972528 -2.663785 -4.3145031 8.133400 2.6647379 -8.633523 0.6939585 -3.272497 -10.1223780 5.630238 7.116551 -2.228281 -6.5028806 5.9995627 -8.207387 -12.7108934 1.667751 4.609682 -3.084449 -10.8749545 -0.3114365 5.713004 2.8365826 -11.923884 3.240808 -3.5395159 5.826357 -4.634728 4.738245 -8.8089280 16.4072536 -0.7373595 -1.6018621 4.4056361 0.8893443 2.0348258 0.852217 -0.3086403 3.646996 0.4364495 -1.826568 -3.4731806 -3.9348135 -0.5811081 1.7101432 0.8812727 6.453960 -4.734093 1.629992 5.6784378 0.0286605 -4.1419281 -0.1399651 3.1493706 -2.039224 3.469285 -2.6305510 -1.938967 -1.496469 -4.617023 -4.934805 1.1523814 0.8361132 1.7865721 5.1844141 2.309614 -0.8349469 1.0222474 -1.4469319 -0.6541372 -0.2519644 -3.1910250 3.1276149 1.3219716 1.983431 0.1884438 -3.240764 -0.1338777 -3.795210 -0.0371808 0.2254558 -0.5734539 6.6982115 -1.948371 -0.8464765 -2.1072545 -1.227587 1.0384161 7.6153681 0.2300076 -4.0147477 -1.571888 -1.6400553 -0.321446 -3.087306 -3.6129438 2.7169033 1.5273598 -0.4557513 -2.786283 -2.1292352 5.1661846 -1.776873 3.4655126 2.894149 1.2161070 1.0247744 2.9033040 3.9909900 4.0585854 0.3207035 -2.2705482 6.7176116 1.1721471 -0.8478128 -1.3453292 -2.3075205 -0.5298626 0.8331240 0.1836416 0.5786881 -7.189932 1.2823490 3.0502958 0.9555872 0.2229662 1.2536385 -0.8092454 -1.5207688 3.4682079 1.765212 -0.3423818 0.2281033 -1.6530140 -2.8254190 0.6434609 -0.2363464 -0.5543145 -1.7254465 -2.3661498 0.5857842 2.3737308 3.5940571 -1.6444701 1.1837954 3.6240175 -1.4107977 -1.7127951 -1.6914581 -4.0885308 -2.2783191 1.3653163 -3.9159521 -0.0144027 1.5175300 -2.4487256 0.4717846 0.4470153 1.5636875 1.4553706 -0.5396413 0.0881128 -1.2963271 2.1973051 -1.8124818 4.0629009 -2.3725436 -4.6538589 -1.3128391 0.7435039 1.3546304 -2.8297862 -0.4545215 -0.7017128 0.3318608 0.1274555 -0.6624369 -2.3013407 -2.6165970 1.9132150 0.6421786 0.4952508 -3.1303906 -1.7653060 1.1712350 -2.7385179 1.1521210 1.3925465 2.5564892 3.0870678 -0.3470571 1.9213155 -3.6767260 1.9949949 -1.6221065 1.3557443 -0.6482301 -0.5626711 2.199956 1.5242459 -1.4905082 -2.9286860 -0.1745429 -1.1537650 -2.2698679 1.0875545 -2.0233393 1.5312580 0.7581935 1.7590888 0.9975656 4.8352504 0.3194420 0.4976907 -0.9563207 -0.8201784 2.6117756 1.8544743 -0.2410170 2.503778 -0.3394768 -0.2889614 1.8026039 3.4371930 1.5834675 -1.7960852 -0.4575736 -1.4517377 0.7097568 -1.8682203 4.0755764 -0.4906318 -2.3568794 2.1684467 -1.3734331 -2.6250400 -0.0612973 -3.0280173 -1.7420832 0.1504280 -0.6867807 2.0833745 -0.9737571 -0.2094934 -0.6632931 1.2389865 0.5143593 1.0909215 -0.2349833 -0.1083014 0.6146779 -1.6708144 1.2834973 -0.4396954 2.2998462 1.5437930 -1.2550435 0.7677388 0.9026181 0.4035457 2.2361561 -0.9969775 0.7546209 -0.5605554 -1.5001540 1.054084 -1.5181469 -1.9789171 2.0898286 -0.7925307 0.2257172 1.9269539 -1.3569669 2.5310667 0.4080292 -0.9157637 1.4247828 -1.2621746 -2.9035378 1.3175299 2.4289002 0.3226050 -1.1509760 -2.2121022 -0.8199612 0.3113948 2.5062439 1.0242483 -0.5190892 -1.1075824 -1.3478037 1.5526570 0.0450236 1.7632775 0.0037133 1.1596697 -0.2411466 2.2054470 1.1377463 -1.6304295 1.4668948 -0.5743106 -0.0551868 -1.8541020 0.8392452 -0.7281738 0.0155533 0.8813552 2.1557175 0.2625946 -0.8776701 -0.8909212 4.3172020 -1.2001009 -0.6111246 0.1318204 -1.9110584 -2.1043975 0.0312611 1.2661946 0.3654030 1.3098698 0.8989996 -0.4536730 -0.9145911 -0.5036551 2.0629831 -0.5074863 -2.1474454 -0.7681624 0.6080273 -0.3467066 -0.5848758 -0.4529507 -0.9775434 0.7693713 0.6972219 0.0661227 -1.5894500 -0.9421117

additional_variables

First look at the dataset.

There are many null values but in general the data looks fine apart of the date values.

This is the head
Date V6409 V3529 V649 V7129 V2089 V1369 V4969 V5689 V7849 V2809 V4970 V650 V7130 V2810 V4249 V5690 V3530 V6410 V2090 V7850 V360 V4250 V1801 V1370 V361 V1800 V3961 V4680 V3240 V3241 V7561 V2521 V6120 V6841 V1080 V7560 V5401 V6121 V634 V5400 V3960 V633 V2793 V6393 V2520 V6840 V1081 V4681 V2826 V4986 V666 V7833 V6265 V3546 V5705 V2106 V4265 V7146 V665 V6426 V2825 V1385 V2794 V7145 V7866 V6425 V3545 V2105 V3385 V3513 V4985 V7834 V1353 V5706 V1386 V5674 V1354 V2505 V6394 V5673 V4266 V7865 V4953 V505 V4234 V4233 V3514 V345 V6985 V1225 V2073 V1785 V1817 V377 V2504 V6137 V6825 V2074 V4697 V3977
19940101 0.00 0.0 0.00 0 0.00 0.00 0.00 NA 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0 0.0 0.00 0.0 0 0 0.00 0 0.00 0 0 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0 0.00 0 0 NA 0.00 0 0.00 0 0.00 0 0.00 0.00 0 0.00 0.00 0.00 0 0 0 0.00 0 0 0.00 0 0 0.00 0 0.00 0.00 0
19940102 0.37 0.1 0.14 0 0.04 0.42 0.04 0 0.17 0.05 0.04 0.14 0.08 0.12 0.2 0 0.2 0.43 0.2 NA 0 0.31 0 0.58 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.60 0.29 0.58 0 0.08 0.80 0.32 0.49 0.42 0.97 0.65 0.93 0.56 1.30 0 0.35 0.57 0.90 0.90 0.25 0.01 0 0.27 0 0 0.38 1.12 0 0.01 0 0.01 0 0.56 0.61 0 0.02 0.03 0.04 0 0 0 0.02 0 0 0.02 0 0 0.00 0 0.01 0.01 0
19940103 0.00 0.0 0.00 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0 0.0 0.00 0.0 0 0 0.00 0 0.00 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 NA 0.00 0 0.00 0.00 0.00 0.04 0.00 0.00 NA 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0.01 0.00 0 0.00 0 0 0.00 0.00 0 0.00 0 0.00 0 0.00 0.00 0 0.00 0.00 0.00 0 0 0 0.00 0 0 0.00 0 0 NA 0 0.00 0.00 0
19940104 0.00 0.0 0.00 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0.0 0 0.0 0.00 0.0 0 0 0.00 0 0.00 0 0 0 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0 0 0 0 0 0.00 NA 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 NA 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0 0.00 0 0 0.00 0.00 0 0.00 0 0.00 0 0.00 0.00 0 0.00 0.00 0.00 0 0 0 NA NA 0 0.00 0 0 0.00 0 0.00 0.00 0
19940105 0.00 0.0 0.00 0 0.00 0.00 0.00 0 NA NA NA 0.00 0.00 0.00 0.0 0 0.0 0.00 0.0 0 0 0.00 0 NA 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00 NA 0.00 0 0.00 0.00 0.00 0.00 0.00 NA 0.00 0.00 0.00 0.00 0 0.00 0.00 0.00 0.00 0.00 0.00 0 0.00 0 0 0.00 0.00 0 0.00 0 0.00 0 0.00 0.00 0 0.00 0.00 0.00 0 0 0 0.00 0 0 0.00 0 0 0.00 0 0.00 0.00 0
19940106 0.00 0.0 0.00 0 0.00 0.00 0.00 0 0.00 NA 0.00 0.00 0.00 0.00 0.0 0 0.0 0.00 0.0 0 0 0.00 0 0.00 0 0 0 0 0 0 0 0 0 0 NA NA 0 0 0 NA 0 0 NA 0 0 0 0 0 0.03 0.01 0.01 0 0.00 0.02 0.01 0.08 0.00 0.00 0.05 0.09 0.11 0.01 0 0.00 0.00 0.14 0.04 0.31 0.00 0 0.11 0 0 0.00 0.00 0 0.00 0 0.00 0 0.00 0.03 0 0.00 0.00 0.00 0 0 0 0.00 0 0 0.00 0 0 0.01 0 0.00 0.00 0
This is the tail
Date V6409 V3529 V649 V7129 V2089 V1369 V4969 V5689 V7849 V2809 V4970 V650 V7130 V2810 V4249 V5690 V3530 V6410 V2090 V7850 V360 V4250 V1801 V1370 V361 V1800 V3961 V4680 V3240 V3241 V7561 V2521 V6120 V6841 V1080 V7560 V5401 V6121 V634 V5400 V3960 V633 V2793 V6393 V2520 V6840 V1081 V4681 V2826 V4986 V666 V7833 V6265 V3546 V5705 V2106 V4265 V7146 V665 V6426 V2825 V1385 V2794 V7145 V7866 V6425 V3545 V2105 V3385 V3513 V4985 V7834 V1353 V5706 V1386 V5674 V1354 V2505 V6394 V5673 V4266 V7865 V4953 V505 V4234 V4233 V3514 V345 V6985 V1225 V2073 V1785 V1817 V377 V2504 V6137 V6825 V2074 V4697 V3977
6904 20121125 0 0 0 NA 0 0 0 0 0 0 NA 0.00 0.0 0.0 0 0 0.0 0.00 NA 0.00 0 0.00 0.00 0.00 0.00 0 0.00 NA 0 0.0 0.0 0.0 0 0.00 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0.00 0.0 0.0 0.00 0.00 0.0 0 0.0 0 0.0 0 0.0 0.00 0.00 0 0.00 0 0 0.00 0.00 0 0.0 0.00 0 0.00 0.0 0 0.0 0.0 0 0.00 0.0 0.00 0 0.0 0.00 0 NA 0.00 0 NA 0 0 NA 0 0.00 0.00 0.0 0 0.00 0 0 0.0 0.00
6905 20121126 0 0 0 0 0 0 0 0 0 0 0.04 NA 0.1 0.1 0 0 0.0 0.04 0 0.20 0 0.05 0.01 0.20 0.02 0 0.03 0 0 0.0 0.1 0.1 0 0.01 0 0 0 0.02 0.07 0 0 0 0 0 0 0 0.03 0.0 0.1 0.00 0.03 0.1 0 0.0 0 0.0 0 0.1 0.00 0.02 0 0.00 0 0 0.00 0.00 0 0.0 0.00 0 0.00 0.3 0 0.0 0.0 0 0.30 0.0 0.05 0 0.0 0.00 0 0 0.11 0 0 0 0 0 0 0.00 0.00 0.0 0 0.01 0 NA 0.0 0.00
6906 20121127 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0.0 0.0 0 0 0.0 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0 0.00 0 0 0.0 0.0 0.0 0 0.00 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0.00 0.0 0.0 0.00 0.00 0.0 0 0.0 0 0.0 0 0.0 0.00 0.00 0 0.00 0 0 NA 0.00 0 0.0 0.00 0 0.00 0.0 0 0.0 0.0 0 0.00 0.0 0.00 0 0.0 0.00 NA 0 0.00 0 0 0 0 0 0 0.00 0.00 0.0 0 0.00 0 0 0.0 0.00
6907 20121128 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0.0 0.0 0 0 0.0 0.00 0 0.00 0 0.00 0.00 0.00 0.00 0 0.00 0 NA 0.0 0.0 0.0 0 0.00 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0.00 0.0 0.0 0.00 0.00 0.0 0 0.0 0 0.0 0 0.0 0.00 0.00 0 0.00 0 0 0.00 0.00 0 0.0 0.00 0 0.00 0.0 0 0.0 0.0 0 0.00 0.0 0.00 NA 0.0 0.00 0 0 0.00 0 0 0 0 0 0 0.00 0.00 0.0 0 0.00 0 0 0.0 0.00
6908 20121129 0 0 0 0 0 0 0 0 0 0 0.00 0.00 0.0 0.0 0 0 0.0 NA 0 0.00 0 0.00 0.00 0.00 0.00 0 0.00 0 0 0.0 0.0 0.0 0 0.00 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0.00 0.0 0.0 0.00 0.00 0.0 0 0.0 0 0.0 0 0.0 NA 0.00 0 0.00 0 0 0.00 0.00 0 0.0 0.00 0 0.00 0.0 0 0.0 0.0 0 0.00 0.0 0.00 0 0.0 0.00 0 0 0.00 0 0 0 0 0 0 0.00 0.00 0.0 0 0.00 0 0 0.0 0.00
6909 20121130 0 0 0 0 0 0 0 0 NA 0 0.15 0.16 0.0 0.2 0 0 0.2 0.01 NA 0.06 0 0.00 0.07 0.13 0.00 0 0.00 0 0 0.1 0.0 0.0 0 0.00 0 NA 0 0.00 0.01 0 0 0 0 0 0 0 0.02 0.1 0.2 0.23 0.26 0.0 0 0.3 0 0.3 0 0.0 0.08 0.13 0 0.02 0 0 0.26 0.01 0 0.1 0.03 0 0.11 0.0 0 0.1 0.2 0 0.01 0.1 0.00 0 0.1 0.16 0 0 0.00 0 0 0 0 0 0 0.04 0.22 0.1 0 0.00 0 0 0.3 0.02

station_info

First look at the dataset and below the map with the location of the data.

The data looks fine.

This is the head
stid nlat elon elev
ACME 34.80833 -98.02325 397
ADAX 34.79851 -96.66909 295
ALTU 34.58722 -99.33808 416
APAC 34.91418 -98.29216 440
ARNE 36.07204 -99.90308 719
BEAV 36.80253 -100.53012 758
This is the tail
stid nlat elon elev
93 WEAT 35.50830 -98.77509 538
94 WEST 36.01100 -94.64496 348
95 WILB 34.90092 -95.34805 199
96 WIST 34.98426 -94.68778 143
97 WOOD 36.42329 -99.41682 625
98 WYNO 36.51806 -96.34222 269

Oklahoma solar station location

leaflet(station_info) %>%
  addTiles() %>%
  addMarkers(lng= ~elon, lat= ~nlat, popup= ~stid)

Feature transformation

In conclusion, there are many null values in ‘solar_dataset’ and in ‘additional_variables’.

See how many null values I need to work with.

solar_dataset

This dataset includes many nulls in addition there is a PCA which do not include null values.

Date ACME ADAX ALTU APAC ARNE BEAV BESS BIXB BLAC BOIS BOWL BREC BRIS BUFF BURB BURN BUTL BYAR 0 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 CAMA CENT CHAN CHER CHEY CHIC CLAY CLOU COOK COPA DURA ELRE ERIC EUFA FAIR FORA FREE FTCB GOOD 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 GUTH HASK HINT HOBA HOLL HOOK HUGO IDAB JAYX KENT KETC LAHO LANE MADI MANG MARE MAYR MCAL MEDF 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 MEDI MIAM MINC MTHE NEWK NINN NOWA OILT OKEM OKMU PAUL PAWN PERK PRYO PUTN REDR RETR RING SALL 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 SEIL SHAW SKIA SLAP SPEN STIG STIL STUA SULP TAHL TALI TIPT TISH VINI WASH WATO WAUR WEAT WEST 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 1796 WILB WIST WOOD WYNO PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15 1796 1796 1796 1796 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC16 PC17 PC18 PC19 PC20 PC21 PC22 PC23 PC24 PC25 PC26 PC27 PC28 PC29 PC30 PC31 PC32 PC33 PC34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC35 PC36 PC37 PC38 PC39 PC40 PC41 PC42 PC43 PC44 PC45 PC46 PC47 PC48 PC49 PC50 PC51 PC52 PC53 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC54 PC55 PC56 PC57 PC58 PC59 PC60 PC61 PC62 PC63 PC64 PC65 PC66 PC67 PC68 PC69 PC70 PC71 PC72 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC73 PC74 PC75 PC76 PC77 PC78 PC79 PC80 PC81 PC82 PC83 PC84 PC85 PC86 PC87 PC88 PC89 PC90 PC91 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC92 PC93 PC94 PC95 PC96 PC97 PC98 PC99 PC100 PC101 PC102 PC103 PC104 PC105 PC106 PC107 PC108 PC109 PC110 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC111 PC112 PC113 PC114 PC115 PC116 PC117 PC118 PC119 PC120 PC121 PC122 PC123 PC124 PC125 PC126 PC127 PC128 PC129 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC130 PC131 PC132 PC133 PC134 PC135 PC136 PC137 PC138 PC139 PC140 PC141 PC142 PC143 PC144 PC145 PC146 PC147 PC148 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC149 PC150 PC151 PC152 PC153 PC154 PC155 PC156 PC157 PC158 PC159 PC160 PC161 PC162 PC163 PC164 PC165 PC166 PC167 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC168 PC169 PC170 PC171 PC172 PC173 PC174 PC175 PC176 PC177 PC178 PC179 PC180 PC181 PC182 PC183 PC184 PC185 PC186 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC187 PC188 PC189 PC190 PC191 PC192 PC193 PC194 PC195 PC196 PC197 PC198 PC199 PC200 PC201 PC202 PC203 PC204 PC205 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC206 PC207 PC208 PC209 PC210 PC211 PC212 PC213 PC214 PC215 PC216 PC217 PC218 PC219 PC220 PC221 PC222 PC223 PC224 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC225 PC226 PC227 PC228 PC229 PC230 PC231 PC232 PC233 PC234 PC235 PC236 PC237 PC238 PC239 PC240 PC241 PC242 PC243 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC244 PC245 PC246 PC247 PC248 PC249 PC250 PC251 PC252 PC253 PC254 PC255 PC256 PC257 PC258 PC259 PC260 PC261 PC262 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC263 PC264 PC265 PC266 PC267 PC268 PC269 PC270 PC271 PC272 PC273 PC274 PC275 PC276 PC277 PC278 PC279 PC280 PC281 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC282 PC283 PC284 PC285 PC286 PC287 PC288 PC289 PC290 PC291 PC292 PC293 PC294 PC295 PC296 PC297 PC298 PC299 PC300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC301 PC302 PC303 PC304 PC305 PC306 PC307 PC308 PC309 PC310 PC311 PC312 PC313 PC314 PC315 PC316 PC317 PC318 PC319 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC320 PC321 PC322 PC323 PC324 PC325 PC326 PC327 PC328 PC329 PC330 PC331 PC332 PC333 PC334 PC335 PC336 PC337 PC338 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 PC339 PC340 PC341 PC342 PC343 PC344 PC345 PC346 PC347 PC348 PC349 PC350 PC351 PC352 PC353 PC354 PC355 PC356 PC357 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

additional_variables

This dataset includes many nulls.

Date V6409 V3529 V649 V7129 V2089 V1369 V4969 V5689 V7849 V2809 V4970 V650 V7130 V2810 V4249 V5690 V3530 V6410 0 430 33 138 457 198 11 268 443 135 337 302 465 41 470 364 388 189 426 V2090 V7850 V360 V4250 V1801 V1370 V361 V1800 V3961 V4680 V3240 V3241 V7561 V2521 V6120 V6841 V1080 V7560 V5401 472 353 278 37 70 439 426 47 88 228 349 41 247 416 271 348 264 263 360 V6121 V634 V5400 V3960 V633 V2793 V6393 V2520 V6840 V1081 V4681 V2826 V4986 V666 V7833 V6265 V3546 V5705 V2106 26 286 35 458 284 472 33 125 375 317 190 371 335 462 195 228 435 173 317 V4265 V7146 V665 V6426 V2825 V1385 V2794 V7145 V7866 V6425 V3545 V2105 V3385 V3513 V4985 V7834 V1353 V5706 V1386 116 218 465 239 31 298 332 175 475 484 461 402 90 482 111 311 268 270 107 V5674 V1354 V2505 V6394 V5673 V4266 V7865 V4953 V505 V4234 V4233 V3514 V345 V6985 V1225 V2073 V1785 V1817 V377 457 382 116 67 266 406 448 209 314 123 15 459 381 294 468 160 169 34 128 V2504 V6137 V6825 V2074 V4697 V3977 292 294 173 292 115 127

station_info

This dataset does not include nulls.

stid nlat elon elev 0 0 0 0

Feature cleaning and visualization

From here on, I will start digging only into the solar_dataset.

Clearly the datasets ‘solar_dataset’ and ‘additional_variables’ have many null values. These will be dropped.

In addition I want to see what is the featureclass for the ‘Date’ column.

[1] “character”

Since the Date featureis a character class, I need to transform this.

[1] “Date” [1] “integer” [1] “numeric” [1] “character”

After inspecting 3 different features, it’s evident that the data has strong outliers. This should be clipped to get more visibility on the data.

It’s also clear that based on the season, there is much more solar production. The end of the spring and the beginning of the sumer season seems particularly great for solar energy production. Which makes sense. A good idea would be to run mantainance during winter or autumn for high performance during peak seasons.

In addition, it is clear that a boxplot will not give much information based on a single feature.

Note:

  • The colors used are not color blind friendly.
  • When seeing the data based on quarters, the data is siloed. This is the reason why the year is not investigated. Clearly, the linear dates give more information.
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The boxplots do not give much information on the data. Clearly there is a strong skew when looking at all the data and there is no clear overview due to the amount of features.

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It is confirmed that the data is extremely skewed. With this data it will be difficult to plot any kind of graphs. It would be important to first exclude in a way most of the outliers.

Note: For this data, outliers are of outmost importance as the stronger the solar energy registered is, the higher the energy production and also the higher the potential of warmer days. Which can potentially indicate forest fires if linked with other forest fire relevant data.

Remove outliers in every column

To remove the outliers, I have decided to use the date.

I have identified with the ‘summary(solar_dataset_new)’ function, that I can see the quartiles and will drop the rows based on this. Anything between the Q25 and Q75 will be kept.

  • Q25%: 1997-07-02

  • Q75%: 2004-07-01

    row col

    [1,] 1279 1 row col [1,] 3835 1

Drop the PCA features and check the graph again and re-draw the boxplot for all the data

In this step I separate the features of the PCA and the other non-PCA features.

When drawing the PCA in a boxplot and while reducing the amount of features shown manually, you can identify that the best number of PCAs would be around 4 and 5 because these seem to include the highest variance / information.

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Without PCA and outliers, there is more visibility on the data. It is somewhat equally distributed among the stations. Some seem to generate more energy than others.

After removing the outliers and the PCA, I want to see the new shape of the features.

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Dropping the outliers did not help much, I still see a strong skew in the data.

This indicated that the variance is high and using PCA or any other dimensionality reduction model would be a great way to move forward when running a ML model.

Compared to the earlier plots, there seems that the energy production slightly decreased after the year 2004.

See correlation

All features are highly correlated.

An assumption would be, that due to the different locations of the station the solar data is collected at a similar temperature / level while the earth moves around the sun. Some have a negative correlation, probably due to the opposite location or because this could be placed in an area where more clouds cover the sky.

Note: Correlation is not causation, therefore these are only assumptions.

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See the distribution of the new data without outliers

The normal dsitribution confirms again the assumption that the data is not normally distributed and strongly skewed. Maybe to predict the next solar data, a model like ARIMA would be used. In case this data would be used to predict the next solar energy level.

Even after I dropped the outliers. For the data to predict accurately and for a model to generalize properly, I will need to select a model that can work well with high variance. I could also try to add bias to the model to reduce the variance or collect more data / artificially create more data. However, by doing so, the assumption would only be that the strong variation in the curves would flatten it and not yielding much more insights.

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Conclusion

In conclusion, I have confirmed that the data is normal for the season with strong outliers. Due to the high variance, a model that can handle the variance or using dimensionality reduction, will help to predict better. This has been shown based on the distribution per day over the years and the correlation between the stations.

As mentioned, probably a good way to predict the next generation of solar energy a model like ARIMA could be used, as this can handle well simple forecasting for data that is strongly skewed. Apart of any other machine learning model that could be also applied.